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Knowledge-Based Systems 52 (2013) 236–245

Contents lists available at ScienceDirect

Knowledge-Based Systems

journal homepage: www.elsevier .com/locate /knosys

A low-cost screening method for the detection of the carotid artery
diseases

0950-7051/$ – see front matter

http://dx.doi.org/10.1016/j.knosys.2013.08.007

⇑ Corresponding author. Tel.: +20 1001426485.
E-mail addresses: ahmed_sadik@h-eng.helwan.edu.eg (A.F. Seddik), doaashaw-

ky@staff.cu.edu.eg (D.M. Shawky).

Ahmed F. Seddik a, Doaa M. Shawky b,⇑
a Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt
b Engineering Mathematics Department, Faculty of Engineering, Cairo University, 12613 Giza, Egypt

a r t i c l e i n f o

Article history:
Received 24 February 2013
Received in revised form 26 July 2013
Accepted 2 August 2013
Available online 13 August 2013

Keywords:
Automatic diagnosis
Carotid artery diseases
Doppler signal classification
Artificial neural networks
K-nearest neighbor

a b s t r a c t

Carotid artery diseases are defined as the narrowing or the blockage of the carotid arteries. These two
conditions are called carotid artery stenosis or occlusion respectively. Stenosis and occlusion are usually
caused by cholesterol deposits and fatty substances which are called plaque. In addition, they represent
significant causes of strokes. Thus, they should be a part of regular physical examinations. An important
and preliminary diagnosis is to listen to the arteries in the neck using a stethoscope or a Doppler ultra-
sound (US) device. However, it is sometimes very difficult for a non-professional physician to differenti-
ate between a normal and an abnormal sound due to blood flow blockage.

This paper presents a low-cost efficient method that can be used in the automatic screening of carotid
artery diseases, especially in areas with high population. Doppler US signals are preprocessed for noise
elimination. Then, some features for normal, stenosis and occlusion signals are extracted from the fre-
quency domain of these signals using their spectrograms. A multi-layer feed forward neural-network
(MLFFNN) and a k-nearest neighbor (KNN) classifiers were used to automatically diagnose the input sig-
nals. The approach is applied to 72 samples divided into three equal sets which represent the three main
classes to be identified, i.e., normal, stenosis and occlusion patterns. We used in the training phase 75% of
each set and the rest was used in the test phase. Experimental results show the simplicity and efficiency
of the presented approach for automatic diagnosis of carotid artery diseases. The maximum obtained
classification accuracies are 91.67%, 100%, and 95.89% for the normal, stenosis and occlusion patterns
respectively when the MLFFNN classifier is used. In comparison with similar approaches, the proposed
approach is less complex, hence runs faster which suggests its suitability as an efficient screening method
for the detection of carotid artery diseases.

1. Introduction

Vascular diseases are guided primarily by the history and phys-
ical examination. However, there is a need for non-invasive inves-
tigations to compensate the lack of expert physicians and the high
cost diagnosis methods. This is especially true in rural areas and
developing countries, also for countries of high populations.

Carotid artery diseases are among the most common vascular
diseases. A Carotid artery disease occurs when the major arteries
in the neck become narrowed (stenosis) or blocked (occlusion).
This problem happens frequently as people age and it is a common
cause to strokes [1]. There are many techniques for the diagnosis of
carotid artery diseases. Doppler ultrasonography is one of the sim-
plest and low-cost methods for detecting carotid artery diseases.

This paper presents an approach to diagnosing carotid artery
stenosis and occlusion using Doppler ultrasound signals. A data
set that contains sound waves for normal carotid artery, carotid ar-
tery stenosis and occlusion is used. Some features are extracted
from the spectrogram of Doppler US signals. Then, using these fea-
tures we trained a feed-forward neural network to classify the
sound waves into normal, stenosis or occlusion. The classifier has
a total accuracy of 95.48%. In addition, a KNN classifier is used,
however, the classification accuracy is 94%. The Doppler sound
waves rather than images are usually collected at a much lower
cost. In addition, only five features are used and yet, the obtained
results are very satisfactory. Thus, the proposed approach is less
complex and less expensive than similar works which utilize
Doppler ultrasonography and image processing techniques. Since
a good screening method should be fast, affordable and available
to a large number of populations [2], the simplicity of the proposed
approach suggests its suitability as an efficient screening method
especially in developing countries and rural areas where it is infea-

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A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245 237

sible to perform other diagnostic tests that need to be done by
medical experts.

The rest of the paper is organized as follows. Section 2 intro-
duces a background on some related concepts. In Section 3, the
proposed approach is presented in detail. Section 4 presents the
performed experimental study and discusses the obtained results.
In Section 5, a survey on the related research is presented. Finally,
Section 6 presents the conclusions and highlights some directions
for the future work.

2. Carotid artery diseases: a background

As shown in Fig. 1, the carotid arteries are two large blood ves-
sels that supply blood to the front part of the brain where major
functions such as thinking, speech and motor functions reside
[3]. Carotid artery disease is a disease in which a waxy substance
called plaque builds up inside the carotid arteries. If plaque builds
up in the arteries (the condition is called atherosclerosis) it hard-
ens and narrows the arteries. This may limit the flow of oxygen-
rich blood to the brain and affect major functionalities of the body.
In some cases it may cause stroke which is a common cause of
death all over the world, especially for people with age over 6

years [4].

Many diagnostic techniques are used to detect carotid artery
stenosis at its early stages. These methods include CT scan and
CT Angiography (CTA) which can show X-ray pictures of the arter-
ies in the neck. Another method for diagnosis is to utilize magnetic
resonance angiography (MRA) which uses radio waves and mag-
netic fields to create detailed images. Some forms of this test can
show moving blood flow and may help evaluate carotid artery dis-
ease. To improve the test’s accuracy, physicians sometimes inject a
material, called gadolinium, to make the arteries more visible [6].
Another dangerous test for diagnosing carotid artery stenosis is
to inject a contrast dye through a catheter that is threaded into
the arteries and then takes X-ray pictures. This test is called angi-
ography. Angiography shows how blood flows through the arteries
and whether they are narrowed. Moreover, carotid duplex ultra-
sound can be used to determine if there is narrowing. Ultrasonog-
raphy is a common method for detecting carotid artery diseases. In
ultrasonography, a probe with two piezoelectric crystals is used.
The transmitting crystal produces ultrasound at a fixed frequency
and the receiving crystal vibrates in response to reflected waves
and produces an output voltage. A two dimensional picture is built
using ultrasound waves reflected from the tissues [7]. The simplest
method for ultrasonography can be done by using a Doppler ultra-
sound device. Ultrasound signals reflected off stationary surfaces
retain the same frequency with which they were transmitted.
However, the frequency of signals reflected from moving objects

Fig. 1. The carotid artery [5].

such as red blood cells shifts in proportion to the velocity of the
target. The output from a continuous wave Doppler ultrasonogra-
phy is usually presented as an audible signal. This signal is heard
by an expert physician to be diagnosed. Although the previous
diagnostic methods are efficient, however, there is a need for a sys-
tem to aid the physician in detecting abnormality in the collected
signals, especially for areas with large populations. Also, an auto-
matic diagnosis is needed to compensate the lack of experts in
some cases.

3. Proposed approach

The basic steps of the proposed approach are depicted in Fig. 2.
The approach consists of the following main steps:

1. Reading the Doppler US files.
2. Pre-processing the analyzed signals.
3. Applying feature extraction techniques.
4. Classifying the analyzed signal in to normal, stenosis or

occlusion.

In the rest of this section, each step of the proposed approach
will be explained in detail.

In the first step, as shown in Fig. 2, the files that contain Doppler
US waves are read into Matlab software package [8]. In the second
step, some preprocessing and noise removal is applied. This is done
using high pass filters as Doppler signals are usually low-frequency
signals. In the third step, the signal spectrogram is generated using
Short-Time Fourier Transform (STFT). STFT is a sequence of fast
Fourier transform (FFT) of windowed data segments, where the
windows are usually allowed to overlap in time, typically by 25–
50%. STFT is a powerful general-purpose tool for audio signal pro-
cessing [9]. The calculations of STFT can be done as follows [10]:

XmðxÞ ¼
X1

n¼�1
xðnÞxðn�mRÞe�jxn ð1Þ

where x(n) is the input signal at time n; m = 1, 2, . . ., N, where N is
the total number of used segments. x(n) is a window function of
length M; Xm(x) is the Discrete-Time Fourier Transform (DTFT) of
windowed data centered about mR; And R is the hop size, in sam-
ples between successive DTFTs.

The input signal x is divided into eight segments. If x cannot be
divided exactly into eight segments, the rest which is at most se-
ven samples is truncated. A Hamming window of length 256 is
used. In addition, a hop size (R) of length 128 is used. After gener-
ating the spectrogram of each signal, a set of features is extracted

Fig. 2. The basic steps of the proposed approach.

238 A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245

from the spectrogram of the analyzed Doppler signals. This set in-
cludes the following five features:

� The maximum of the coefficients generated in the spectrogram
of the signal (Max).
� The minimum of the coefficients generated in the spectrogram

of the signal (Min).
� The mean of the coefficients generated in the spectrogram of

the signal (Mean).
� bRI which is defined as bRI = (Max �Min)/Min.
� bPI which is defined as bPI = (Max �Min)/Mean.

The features bRI and bPI are related to the resistivity index (RI)
and the pulsatility index (PI) respectively. These two indices are
derived from Doppler sonograms [11] and they are reflections of
the resistance to flow. PI is computed according to the method of
Gosling and King [12] as follows:

PI ¼ ðVPS � VMDÞ=Vmean ð2Þ

where VPS is the blood flow velocity at peak systole, VMD is the
blood flow velocity at maximum diastolic deflection, and Vmean is
the velocity time averaged over the cardiac cycle. RI is calculated
by the formula of Planiot et al. [13] as follows:

RI ¼ ðVPS � VEDÞ=VPS ð3Þ

where VED is the blood flow velocity at end-diastole. Although, as
stated in [14], PI and RI values are not good predictors for Doppler
waveforms, bPI and bRI which are analogous to PI and RI respectively
have proven to be good features for Doppler spectrograms in the
proposed approach.

After extracting the features, a classifier is trained to classify the
signals which represent normal, stenosis and occlusion cases. Two
different classifiers are used; a neural-network based classifier, and
a KNN classifier. During the training phase, only 75% of the data is
used. The other 25% of the data is used in the test phase.

In the last step of the proposed method, to assess the efficiency
of the proposed approach, evaluation of the obtained results is
done. Classification accuracy, sensitivity and specifity of the
trained classifier are calculated. Accuracy is the ratio of correctly
classified patterns over the total number of analyzed patterns.
Meanwhile, sensitivity is the ratio of true positives over the total
number of patterns representing abnormal patterns. The last used
performance measure is the specifity which is the ratio of true neg-
atives over the total number of patterns representing normal
signals.

It should be mentioned that the suggested approach can be
used as a screening method. Screening methods are different from
diagnostic tests. For a screening method to be effective, it should
be sensitive, specific, available to a large number of population,
and acceptable in terms of cost, risk and patient tolerability
[2,15–17]. Also, a screening method should be fast to be performed
on a large number of patients. Thus, it should be simple and yet
efficient.

4. Experimental study

4.1. Used data

The used data set includes 72 samples of complete cardiac cy-
cles divided equally among the three classes; normal, stenosis
and occlusion. The twenty four normal signals are equally divided
between males and females. Meanwhile, the twenty four signals
representing stenosis belong to 14 males and 10 females. Finally,
the occlusion signals belong to 18 males and 6 females. The ages
of the 72 subjects range from 56 to 73 years with an average of

66 years. This data set was collected in Helwan’s University medi-
cal center using a pen probe Doppler US device at 4 MHz.

4.2. Results and discussion

We first perform some exploratory analysis to help us deter-
mine whether the suggested features can be used as good predic-
tors for the Normal, Stenosis and Occlusion classes or not. First,
the wave forms of the three classes are plotted as shown in Figs. 3–
7. It is clear that the wave forms have different characteristics.
Also, it should be mentioned that in the cases of stenosis or occlu-
sion, the physician was able to distinguish whether the heard sig-
nal is near or far from the location of the stenosis or occlusion.
Thus, for Stenosis and Occlusion patterns, two wave forms are
shown for each case since they produce different wave forms
according to the location of the Doppler device. Although these dis-
eases have different wave forms as shown in Figs. 4–7, however,
the proposed classifiers are trained to detect the existence of the
stenosis or occlusion irrespective of their exact location. Thus, in
the evaluation step, we merged these patterns to obtain signals
representing only the three main classes to be identified. The spec-
trograms for the above wave forms are shown in Figs. 8–12.

Fig. 13 shows the mappings of the three the main classes to be
identified using the five extracted features; bRI , bPI; Max, Min, and
Mean respectively. It should be noticed that using the feature
Min, for instance, a large number of patterns for the Normal and
Occlusion classes are overlapped. Hence, the Min feature has a
low separation power between these classes. On the other hand,
the bRI feature has a better separation power as less number of pat-
terns belonging to the different classes are overlapped.

To further assess the separation power of the used features, we
performed t-test with significance level of 0.05 [18] for every pair
of the three main classes using the five extracted features. The t-
test examines the null hypothesis that the represented patterns
using only feature i and feature j have the same mean against
the hypothesis that the two means are different and hence the sep-
aration power is higher. The lower the p-value, the better the sep-
aration power is. Table 1 shows the obtained p-values of each
different pair of the Normal, Stenosis, and Occlusion patterns. It
should be noticed that the p-values are very small except for the
Min feature for Normal–Occlusion pair; it is above the significance
level. The same is true for the Mean feature for Normal–Stenosis
pair. Otherwise, the zero or almost zero p-values for the other cases
suggest the suitability of the extracted features as good predictors
of the three classes to be identified.

4.2.1. Neural network based classifier
The implemented network employs multi-layer feed-forward

back propagation (MLFBP) with one hidden layer of five neurons.
The MLFFBP network is trained using the training set which con-
tains 75% of the total dataset. In comparison to similar approaches
e.g. [14], the training phase is very efficient (has low complexity) as
it reaches the minimum mean square error (MSE) between the net-
work output and the desired output in only 14 epochs. As shown in
Fig. 14, after 14 epochs, the test error starts to increase due to over
fitting, thus the training phase should be stopped at this point. Of
course part of this is due to lower training set size. However, the
low dimensionality of the features set, in addition to good features
selections are two other important factors for the efficiency of any
classification problem.

Table 2 presents the confusion matrix for the classification
problem. As shown in the table, only three patterns are misclassi-
fied. Also, the classification accuracies for Normal, Stenosis and
Occlusion classes are 91.67%, 100%, and 95.83% respectively. In
addition, the total classification accuracy is 95.48% which is a very

0 0.5 1 1.5 2 2.5 3 3.5 4

x 105

–

0.5

–

0.4

–

0.3

–

0.2

–

0.1

0.2

0.3

0.4

0.5

Samples

Fr
eq

ue
nc

y
Sh

if
t (

Fig. 3. The normal Doppler US wave form.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 105

-0.3

-0.

-0.2

–

-0.1

–

0.05

0.1

0.15

0.2

Samples

F
re

qu
en

cy
S

hi
ft

(
H

Fig. 4. The over-stenosis Doppler US wave form.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 105

-0.4

-0.3

-0.2

-0.1

0.1

0.2

0.3

Samples

F
re

qu
en

cy
S

hi
ft

(
H

Fig. 5. The stenosis-distant Doppler US wave form.

A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245 239

promising accuracy for such a low-cost and a fast screening
method.

Furthermore, as shown in the confusion matrix, the sensitivity
is 97.9%. Meanwhile, the specifity is 91.7%.

4.2.2. KNN classifier
KNN is a statistical-based classifier that is used to predict the

response of an observation using a non-parametric estimate of

the response distribution of its k nearest neighbors [19]. It is based
on the assumption that the characteristics of members of the same
class should be similar and thus, observations located close to-
gether in covariate (statistical) space are members of the same
class or at least have the same posterior distributions on their
respective classes. The choice of k affects the performance of the
KNN algorithm. To determine the nearest neighbors to a sample,
the relative distance between instances is determined by using a

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 105

–

0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.1

0.2

0.3

0.4

Samples

F
re

qu
en

cy
S

hi
ft

(
H

Fig. 6. The over-occlusion Doppler US wave form.

0 1 2 3 4 5 6 7
x 105

-1

–

0.8

-0.6

-0.4

-0.2

0.2

0.4

0.6

0.8

Samples

F
rq

ue
nc

y
Sh

if
t

(h
z)

Fig. 7. The occlusion-distant Doppler US wave form.

0 200 400 600 800 1000 1200 1400 1600 1800

2000

100

150

200

250

300

350

Frequency (Hz)

Fig. 8. The normal Doppler US spectrogram.

240 A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245

distance metric. We used KNN classifier with k equals to 3 and
Euclidean distance metric to classify the analyzed patterns. A clas-
sification accuracy of 94% is obtained with 94% and 83% for the sen-
sitivity and the specifity respectively. Thus, for the studied
problem, the neural network based classifier had better perfor-
mance than the KNN classifier.

To study the effect of the sample size over the generalization
power, we performed t-test using each feature with the null
hypothesis that the samples come from normal distribution with
population mean that is equal to the sample mean. The test fails
to reject the null hypothesis for the five used features at a signifi-
cance level of 0.05. Moreover, there is a 95% chance of having the

0 500 1000 1500 2000 2

500

100

150

200

250

300

Frequency (Hz)

Fig. 9. The over-stenosis Doppler US spectrogram.

0 500 1000 1500 2000 2500
0

100

150

200

250

Frequency (Hz)

Fig. 10. The stenosis-distant Doppler US spectrogram.

0 500 1000 1500 2000 2500
0

100

200

300

400

500

600

Frequency (Hz)

Fig. 11. The over-occlusion Doppler US spectrogram.

A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245 241

population mean belonging to the confidence interval [364.37,
398.04], [364.36, 398.03], [365.42, 388.41], [0.09, 0.13] and
[13.14, 15.69] for the features bRI , bPI Max, Min and Mean respec-
tively. To evaluate to what degree the used samples represent
the real population, we propose using the following measure:

Degree of Confidence ¼ 1� Length of CI
Mean of CI

� �
� 100% ð4Þ

where CI is the confidence interval generated by the t-test for each
feature. Thus, we are confident by 91.17%, 91.18%, 91.27%, 60.85%
and 82.33% that the features bRI , bPI Max, Min and Mean represent

the real population respectively. To obtain a narrower confidence
interval, more data samples should be used. Hence, more confi-
dence degrees can be obtained.

Table 3 shows our approach in comparison with similar ap-
proaches. In order to be able to compare the complexity of the pre-
sented approaches, we used the number of features which are used
as the inputs to the classifier as an indicator. However, if it was not
available, we used the number of the training epochs instead. In
[21], the author has used the US Doppler signals that were acquired
from mitral valve of the subjects in order to detect vascular heart
diseases. The STFT of the sonograms of the obtained signals were

0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0

1000

2000

3000

4000

5000

6000

Frequency (Hz)

Fig. 12. The occlusion-distant Doppler US spectrogram.

(b)(a)

(e)

0 5 10 15 20 25
200

250

300

350

400

450

500

550

600
First Feature (Ri)

0 5 10 15 20 25
200

250

300

350

400

450

500

550

600
Second Feature (Pi)

0 5 10 15 20 25
0

30
Fifth Feature (Mean)

0 5 10 15 20 25200

250

300

350

400

450

500

550

600
Third Feature (Max)

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45
Fourth Feature (Min)

Fig. 13. Mappings of the data using the five features (a) bRI (b) bPI (c) Mean (d) Max (e) Min.

242 A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245

calculated and 100 sample points were used as inputs to neural
network based classifier with two hidden layers consisting of 25
and 12 neurons. Thus, the objective of the work proposed in [21]
was to detect heart diseases not the carotid artery diseases. Also,
although the approach was based on a neural network classifier,

however the topology consists of 2 hidden layers (in contrast with
the proposed network which consists of only 1 hidden layer) which
adds extra complexity. Also, in [22], the authors employed US
Doppler images rather than audio signals which is costly due to
the needed expensive device. Moreover, the process is computa-

Table 1
The obtained p-values of the five features for different pairs of the three classes.

Class pair/feature Max Min Mean bPI bRI

Normal–Stenosis 0.0000 0.0000 0.0000 0.0002 0.0632
Normal–Occlusion 0.0000 0.0000 0.0000 0.3167 0.0000
Stenosis–Occlusion 0.0000 0.0000 0.0000 0.0008 0.0000

Fig. 14. The training phase curve.

Table 2
The confusion matrix of the used network.

Output/desired Normal Stenosis Occlusion

Normal 22 0 1
Stenosis 0 24 0
Occlusion 2 0 23

Table 3
A comparison between proposed approach and similar works.

Proposed
approach

Uguz
et al.
[20]

Kara [21] Hassan et al. [22]

Detected
classes

Normal,
Stenosis,
Occlusion

Normal,
Stenosis

Normal, Mild
Stenosis,
Severe
Stenosis

Normal,
abnormal

Accuracy (%) 95.48 97.38 96.7 98.4
Sensitivity (%) 97.9 98.53 100 99.15
Specifity (%) 91.7 94.55 93.3 93.94
Input Signals Doppler US

audio
Doppler
US audio

Doppler US
audio

Doppler US
images

Complexity
indicator

5 inputs
extracted
from US
audio signal

Training
in 500
epochs

100 inputs
extracted
from US
audio signal

Min # of features
used is 48
extracted from
US images

A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245 243

tionally complex. Firstly, a preprocessing stage is performed. Sec-
ondly, spatial, wavelets and gray level co-occurrence matrix fea-
tures are extracted from carotid artery ultrasound images.
Thirdly, redundant and less important features are removed from
the features set using genetic search process. Finally, segmentation
process is performed on the reduced features. Thus, although the
proposed approach in [22] gives the best accuracy, it is very com-
plicated which makes it inefficient as a screening method.

It should be noticed that the approach which utilizes Doppler
US images rather than audio signals provided the highest perfor-

mance. However, it is usually very costly in terms of complexity
and money as it requires Doppler ultrasonography imaging device
followed by image processing techniques. This suggests the suit-
ability of these approaches as diagnostic rather than screening
tests. On the other hand, for the techniques which utilize the Dopp-
ler US audio signals, the performance is slightly degraded. Further-
more, as shown in Table 3, there is a slight sacrifice of performance
of the proposed approach. However, this is compensated by the
less complexity and hence the faster to be run test. This makes
the proposed approach more suitable to be used for screening a
large number of population followed by diagnostic tests for only
positive subjects.

5. Related research

The literature contains a lot of research for the detection of car-
otid artery diseases employing various techniques. For instance, in
[23], the authors proposed a model for carotid artery stenting
(CAS) devices using finite element analysis. However, as the
authors mentioned, its results depend on the proper selection of
patients and devices. Moreover in [24], the authors presented an
approach for improving the ability of the identification of plaque
that may produce strokes. The study combines some extracted tex-
ture features form duplex images of 1121 subjects with asymp-
tomatic internal carotid artery stenosis with clinical factors such
as age, BMI, etc. In addition, some morphological features (dark,
bright and medium-brightness regions) associated with plaque
composition are also considered. In [25], a reference data for the
common carotid artery is generated using the envelope waveforms
of blood velocity of 202 healthy subjects. Moreover, in [26], data
mining techniques were employed to explore the relationships
among some factors such as hypertension, cardiac morbidity,
smoking, diabetes, and physical inactivity that can be used in
asymptomatic carotid stenosis. More specifically, genetic algo-
rithms, logistic regression, and Chi-square tests have been applied
to 372 patient subjects. Logistic regression yielded better results
than genetic algorithms. Chen et al. [27], proposed a scheme for
the detection of common carotid artery using some waveforms fea-
tures from ultrasound images. Extracted features were previously
used by physicians to differentiate between normal blood flow
and five types of abnormal blood flow. The automated approach
has an accuracy of 97%. In addition, in [28], a computer model
based on an flow velocity distribution is proposed to generate
Doppler ultrasound signals from blood flow in the vessels with var-
ious stenosis degrees. The factors included in the analysis include
the velocity field from pulsatile blood flow in the stenosed vessels,
sample volume shape and acoustic factors that affect the Doppler
signals. Navier–Stokes equations are analytically solved to calcu-
late the velocity distributions of pulsatile blood flow in the vessels
with various stenosis degrees. Then, power spectral density of the
Doppler signals is estimated. Estimated Doppler signals were close
to the theoretical ones.

Gupta et al. [29] stated that carotid image registration has the
potential to improve the monitoring, quantification and character-
ization of the disease. The authors conclude that ultrasonography
is an important cost-effective and non-invasive imaging modality
which can be used to screen a large number of symptomatic and
asymptomatic patients suspicious of having vulnerable plaques.

Other different modelling techniques also exist in the literature.
For example, in [30], Kefayati et al. provided quantitative measures
of instabilities and turbulence in the carotid artery bifurcation
using proper orthogonal decomposition to visualize complex blood
flow patterns with different stenosis-severity levels. The study
used anatomically generated flow models for Doppler ultrasound
and multi-modality flow studies using developed life-sized
phantoms.

244 A.F. Seddik, D.M. Shawky / Knowledge-Based Systems 52 (2013) 236–245

Being a low-cost non-invasive method to screen patients suspi-
cious of having carotid artery stenosis or occlusion, many studies
in the literature focused on the automatic classification of carotid
artery Doppler signals. For example, in [31] complex-valued Artifi-
cial Neural Network structure is used to classify carotid artery
Doppler signals using Principal Component Analysis and Fuzzy c-
means Clustering as feature extraction methods. Moreover, an ap-
proach that is based on Fast Fourier Transform, Hilbert Transform,
and Welch Method with different window types is proposed in
[32]. The authors investigated effects of window types on classifi-
cation of carotid artery Doppler signals. Many spectral analysis
methods were used by Guler et al. [33]-[34] to extract features
from Doppler signals. These features were then classified using
neural networks. In addition, Latfaoui et al. [35] have used Fourier
transform, wavelet transform and S-transform for the analysis of
carotid and femoral arteries Doppler signal. In [36], the diagnostic
accuracy of US in the detection of high-grade stenosis or occlusion
of the celiac artery was investigated. The results of Doppler US
were compared with those of lateral aortography. The authors con-
cluded that Doppler US can be used as a screening method and that
it can reduce the use of unnecessary, invasive angiography. In addi-
tion, in [37], a review of studies that compared Doppler ultrasound
to CT angiography is performed. Also, in [38], the authors devel-
oped a Discrete Hidden Markov Model system to classify the inter-
nal carotid artery Doppler signals. The proposed system reached
97.38% of classification accuracy. In comparison with the presented
related works, the proposed method is less expensive and less
complex which makes it more suitable as an efficient screening
method for large populations.

6. Conclusion and future work

In this paper, a method for the screening of carotid artery dis-
eases (stenosis and occlusion) is proposed.

The suggested method extracts some features from Doppler US
audio directly. The extracted features are then used to train a neu-
ral network and a KNN classifiers that can classify the US audio as
normal, stenosis or occlusion. The proposed approach reached a
maximum total classification accuracy of 95.48% on the used data
set when a neural network is employed. Although the used data set
in the experimental study is not large enough to generalize the ob-
tained results, however, obtained results are promising and indi-
cate the suitability of the proposed approach as a low-cost
efficient screening method for carotid artery stenosis and
occlusion.

The main contribution of the proposed work is the utilization of
the low-cost pen probe Doppler US audio device instead of the
expensive imaging one. Also, we employed only five features
which reduces the time of the test to the minimum. This makes
the proposed approach more suitable as a screening method for
the carotid artery diseases in comparison with similar approaches.

Future work includes the application of the suggested system
on a larger data set to add more power to the generalization. In
addition, other features may be added to the used features set in
order to further enhance the classification results.

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A low-cost screening method for the detection of the carotid artery diseases

1 Introduction

2 Carotid artery diseases: a background

3 Proposed approach

4 Experimental study

4.1 Used data

4.2 Results and discussion

4.2.1 Neural network based classifier

4.2.2 KNN classifier

5 Related research

6 Conclusion and future work

References

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